Baseline biomarkers for outcome of melanoma patients treated … · 2016. 5. 14. · 1 Baseline biomarkers for outcome of melanoma patients treated with pembrolizumab Benjamin Weide

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Baseline biomarkers for outcome of melanoma patients treated with pembrolizumab

Benjamin Weide 1,2, Alexander Martens 1, Jessica C. Hassel 3,4, Carola Berking 4,5, Michael A.

Postow 6,7, Kees Bisschop 8, Ester Simeone 9, Johanna Mangana 10, Bastian Schilling 4,11, Anna

Maria Di Giacomo 12, Nicole Brenner 13, Katharina Kähler 14, Lucie Heinzerling 15, Ralf Gutzmer

16, Armin Bender 17, Christoffer Gebhardt 4,18,19, Emanuela Romano 20, Friedegund Meier 4,21,

Peter Martus 22, Michele Maio 12, Christian Blank 23, Dirk Schadendorf 4,11, Reinhard Dummer 10,

Paolo A. Ascierto 9, Geke Hospers 8*, Claus Garbe 1,4*, Jedd D. Wolchok 6,7*

*contributed equally

1. Department of Dermatology, University Medical Center Tübingen, Tübingen, Germany

2. Department of Immunology, University of Tübingen, Tübingen, Germany

3. Department of Dermatology and National Center for Tumor Diseases, University Hospital

Heidelberg, Heidelberg, Germany

4. German Cancer Consortium (DKTK), Heidelberg, Germany

5. Department of Dermatology, University Hospital of Munich, Munich, Germany

6. Memorial Sloan Kettering Cancer Center, New York, NY, USA

7. Weill Cornell Medical College, New York, NY, USA

8. Department of Medical Oncology, University Medical Center Groningen, University of

Groningen, Groningen, The Netherlands

9. Istituto Nazionale Tumori Fondazione Pascale, Naples, Italy

10. Department of Dermatology, University of Zürich, Zürich, Switzerland

11. Department of Dermatology, University Hospital, West German Cancer Center, University

Duisburg-Essen, Essen, Germany

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12. Division of Medical Oncology and Immunotherapy, University Hospital of Siena, Siena,

Italy

13. Department of Dermatology and Venereology, University Hospital of Cologne, Cologne,

Germany

14. Department of Dermatology, University Hospital Schleswig-Holstein, Kiel, Germany

15. Department of Dermatology, University Hospital Erlangen, Erlangen, Germany

16. Skin Cancer Center, Department of Dermatology, Hannover Medical School, Hannover,

Germany

17. Department of Dermatology and Allergology, University Hospital of Marburg, Marburg,

Germany

18. Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany

19. Department of Dermatology, University Medical Center Mannheim, Ruprecht-Karl

University of Heidelberg, Mannheim, Germany

20. Department of Oncology, Service of Medical Oncology, Research Unit 932, Institut Curie,

Paris, France

21. Department of Dermatology, University Medical Center Dresden, Dresden, Germany

22. Departments of Clinical Epidemiology and Applied Biostatistics, University of Tübingen,

Tübingen, Germany

23. Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The

Netherlands.




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Section: Personalized medicine

Running head: Biomarkers for outcome of pembrolizumab-treated patients

Key words: Melanoma, pembrolizumab, biomarker, prognosis, LDH, RLC, REC

Financial Support: This work was supported by a research grant of the Hiege foundation against

skin cancer, Hamburg, Germany.

Acknowledgements: The following people provided data to the project but are not represented in

the author list: Steven Goetze: Department of Dermatology, Friedrich Schiller University, Jena,

Germany. Cornelia Mauch, Max Schlaak: Department of Dermatology, University Hospital of

Cologne, Cologne, Germany. Frank Meiß: Department of Dermatology, University Medical

Center Freiburg, Freiburg, Germany. Nadine Went: Department of Dermatology, Hannover

Medical School, Hannover, Germany. Dirk Debus, Jasmin Jähner: Department of Dermatology,

Klinikum Nord, Nürnberg, Germany. Claudia Pföhler: Department of Dermatology, Saarland

University Hospital, Homburg, Germany. Suvada Biserovic, Evelyn Dabrowski, Edgar Dippel:

Department of Dermatology, Ludwigshafen Hospital, Ludwigshafen, Germany. Ursula Dietrich:

Department of Dermatology, University Medical Center Dresden, Dresden, Germany. Carsten

Schulz: Department of Dermatology, University Medical Center Heidelberg, Heidelberg,

Germany. Sebastian Haferkamp: Department of Dermatology, University Medical Center

Regensburg, Regensburg, Germany. Carsten Weishaupt: Department of Dermatology, University

Medical School, Münster, Germany. Patrick Terheyden: Department of Dermatology, University

of Lübeck, Lübeck, Germany. Iris Pönitzsch: Department of Dermatology, Medical Faculty of




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the Leipzig University, Leipzig, Germany. Amir Khammari: University Hospital, Nantes, France.

Jennifer Landsberg: University Medical Center Bonn, Bonn, Germany. Marcello Curvietto:

Istituto Nazionale Tumori Fondazione Pascale, Naples, Italy. Tabea Wilhelm: Skin Cancer

Center Charité, Berlin, Germany.

Corresponding author:

Benjamin Weide, MD

Dept. of Dermatology, University Medical Center

Liebermeisterstr. 25, 72076 Tübingen, Germany

Tel. +49 70712984555, Fax. +49 7071 295265

[email protected]

Declaration of interests:

BW reports grants from Merck/MSD, during the conduct of the study; grants and personal fees

from Bristol Myers Squibb, personal fees from Philogen, personal fees from Roche, outside the

submitted work; JCH reports personal fees from BMS, MSD, Roche, GSK/Novartis, Amgen,

outside the submitted work; CaB reports personal fees from AstraZeneca, personal fees from

Amgen, personal fees from BMS, personal fees from Roche, personal fees from GSK, personal

fees from MSD, personal fees from Novartis, outside the submitted work; MAP reports grants

from Bristol-Myers Squibb, personal fees from Bristol-Myers Squibb, outside the submitted

work; BS reports other from MSD, outside the submitted work; KK reports personal fees from

MSD, personal fees from BMS, during the conduct of the study; LH reports other from MSD,

other from BMS, other from GSK, other from Roche, outside the submitted work; and speakers

fees from MSD; RG reports grants from Novartis, Pfizer, Roche, Johnson&Johnson, personal




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fees from Roche, BMS, GSK, Novartis, MerckSerono, MSD, Almirall, Amgen, Galderma,

Janssen, Boehringer Ingelheim, Pfizer, personal fees from Roche, BMS, MSD, LEO, GSK,

Amgen, Novartis, Almirall, personal fees from Roche; BMS, MSD, outside the submitted work;

AB reports personal fees from pharmaceutic industry, outside the submitted work; ChG reports

other from BMS, other from GSK, other from Novartis, outside the submitted work; MM reports

grants and personal fees from BMS, grants and personal fees from Roche, personal fees from

GSK, grants and personal fees from MedImmune, outside the submitted work; CB reports

personal fees from MSD, personal fees from BMS, personal fees from GSK, personal fees from

Roche, grants and personal fees from Novartis, outside the submitted work; DS reports personal

fees from Amgen, GSK, BMS, Novartis, Roche, Amgen, Merck, outside the submitted work; RD

reports grants from Novartis, grants and personal fees from Bristol-Myers Squibb, grants and

personal fees from Roche, grants and personal fees from GlaxoSmithKline, personal fees from

Merck Sharp & Dhome, personal fees from Amgen, outside the submitted work; PAA reports

grants and personal fees from Bristol Myers Squibb, grants and personal fees from Roche-

Genentech, personal fees from Merck Sharp & Dohme, grants and personal fees from Ventana,

personal fees from GSK, personal fees from Novartis, personal fees from Amgen, outside the

submitted work; GH reports personal fees and non-financial support from MSD, personal fees

and non-financial support from BMS, personal fees and non-financial support from Roche,

outside the submitted work; CG reports grants and personal fees from Merck, during the conduct

of the study; personal fees from Amgen, grants and personal fees from BMS, personal fees from

Novartis, grants and personal fees from Roche, grants and personal fees from GSK, outside the

submitted work; JW reports grants from Bristol Myers Squibb, grants from Merck, during the

conduct of the study; grants and other from Bristol Myers Squibb, grants and other from Merck,




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outside the submitted work. AM, KB, ES, JM, AMDG, NB, ER, FM, PM have nothing to

disclose.

Word count: 4372

Number of Tables: 3

Number of Figures: 3

This manuscript has not been presented previously




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Statement of translational relevance

This study reports a prognostic model for melanoma patients treated with pembrolizumab

involving four baseline factors easily available in clinical practice: the pattern of visceral

metastases; serum levels of LDH; relative lymphocyte count; and relative eosinophil count. The

probability to survive 12 months after the first dose was 15% in patients with none out of four

favorable factors, in contrast to 84% for patients with all four favorable factors present. Our

results improve patient counselling. Despite the large differences in outcome according to the

number of favorable factors, it remains unclear whether the combination model is predictive of

pembrolizumab treatment benefit. This model warrants investigation in randomized controlled

trials of pembrolizumab to determine whether it has predictive benefit that may help to guide

treatment decisions.




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Abstract

Purpose: Biomarkers for outcome after immune-checkpoint blockade are strongly needed as

these may influence individual treatment selection or sequence. We aimed to identify baseline

factors associated with overall survival (OS) following pembrolizumab treatment in melanoma

patients.

Experimental design: Serum lactate dehydrogenase (LDH), routine blood count parameters, and

clinical characteristics were investigated in 616 patients. Endpoints were OS and best overall

response following pembrolizumab. Kaplan-Meier analysis and Cox regression were applied for

survival analysis.

Results: Relative eosinophil count (REC) ≥1.5%, relative lymphocyte count (RLC) ≥17.5%,

≤2.5-fold elevation of LDH, and the absence of metastasis other than soft-tissue/lung were

associated with favorable OS in the discovery (n=177) and the confirmation (n=182) cohort and

had independent positive impact (all P<0.001). Their independent role was subsequently

confirmed in the validation cohort (n=257; all P<0.01). The number of favorable factors was

strongly associated with prognosis. One-year-OS probabilities of 83.9% vs 14.7% and response

rates of 58.3% vs 3.3% were observed in patients with four out of four compared to those with

none out of four favorable baseline factors present, respectively.

Conclusions: High REC and RLC, low LDH, and absence of metastasis other than soft-

tissue/lung are independent baseline characteristics associated with favorable OS of patients with

melanoma treated with pembrolizumab. Presence of four favorable factors in combination

identifies a subgroup with excellent prognosis. In contrast, patients with no favorable factors

present have a poor prognosis, despite pembrolizumab, and additional treatment advances are still

needed. A potential predictive impact needs to be further investigated.




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Introduction

Antibodies targeting programmed cell death protein-1 (PD-1) represent the second breakthrough

in immune checkpoint blockade therapy of melanoma after approval of ipilimumab. Two PD-1-

blocking antibodies, pembrolizumab and nivolumab have been approved by the FDA/EMEA for

melanoma.

Pembrolizumab demonstrated clinical activity in a variety of solid tumors (1, 2) and improved

overall survival (OS) of melanoma patients compared to ipilimumab (3). For ipilimumab-naïve

patients treated with 10 mg pembrolizumab per kilogram of body weight either every 2 weeks or

every 3 weeks, estimated 12-month survival rates were 68% and 74% and the proportion of

patients with objective responses was 34% and 33%, respectively (3). Nevertheless, only 21-28%

of ipilimumab-pretreated patients exhibit objective responses to pembrolizumab and

approximately 40% die within one year (4, 5). In melanoma, nivolumab was associated with

improved OS and progression-free survival (6) and higher response rates (7) in comparison to

chemotherapy in phase III randomized clinical trials. Moreover, nivolumab alone or combined

with ipilimumab resulted in significantly longer progression-free survival than ipilimumab alone

among previously untreated patients (8).

Thus far, no biomarkers have yet been established to clearly predict clinical benefit from

pembrolizumab. Most studies focused on the immunohistochemical analysis of anti-programmed

cell death ligand-1 (PD-L1) on tumor cells and reported an association between high expression

and clinical responses to nivolumab (9-11) or pembrolizumab (12). However, PD-L1 expression

on tumor cells cannot be used as selection criterion for anti-PD-1 antibody treatment, since

clinical activity was also observed in patients with low/negative PD-L1-expressing tumors, and




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because of differences in the definition of PD-L1 positivity, intra-patient heterogeneity, and

limited assay standardization (11, 13, 14).

The absolute lymphocyte count (ALC) has been reported as biomarker candidate for outcome

after ipilimumab treatment (15, 16) but not for pembrolizumab. Increase in activated T cells

during pembrolizumab or during nivolumab and ipilimumab in combination were reported

previously (13, 17). ALC was not obviously changing upon combined immune-checkpoint

blockade and low ALC did not preclude clinical activity. Clinical responses were correlated with

low levels of circulating myeloid-derived suppressor cells (MDSCs) (17). High NY-ESO-1 and

MART-1–specific T cells at baseline or decreases during treatment as well as increases in

circulating T regulatory cells (Tregs) were associated with disease progression in patients treated

with nivolumab +/- peptide vaccine (11). Preexisting CD8+ T cells in the tumor

microenvironment were required for tumor regression after pembrolizumab (18), while the

presence of an immune infiltrate in early on-treatment samples was even more predictive of

response for PD-1 blockade compared to that in pre-treatment samples in another study (19).

Higher numbers of PD1+ T-cells at baseline and increased PD-L1 expression during treatment

were associated with clinical response in an analysis of sequential tumor biopsies of 24

melanoma patients treated with PD-1 blockade (20).

The aim of the present study was to examine readily available clinical factors and peripheral

blood count parameters to identify pre-treatment factors associated with OS of advanced

melanoma patients treated with pembrolizumab.




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Patients and Methods

Patients

Data from patients treated with pembrolizumab were provided by 30 clinical sites

(Supplementary Table 1). Inclusion criteria were unresectable stage III or stage IV melanoma,

treatment with at least one dose of pembrolizumab, and available data of differential blood counts

and/or LDH from blood draws taken 0-28 days before the first dose of pembrolizumab. Patients

gave their written informed consent for use of clinical data for scientific purposes. This study was

approved by the Ethics Committee, University of Tübingen (approvals 524/2012BO2 and

234/2015BO2).

Study design

Differences in OS according to 15 variables were investigated. These were: gender, age, pattern

of visceral tumor involvement (distant lymph nodes/soft tissue and/or lung only vs involvement

of other organs), and routine blood analyses (LDH, leucocyte count, absolute and relative counts

of lymphocytes, monocytes, eosinophils, basophils and neutrophils). LDH was analyzed by

means of the LDH-ratio (actual value divided by the upper limit of normal). The analysis of the

discovery cohort (recruitment until December 3rd 2014) aimed to identify prognostic factor

candidates. Candidate factors and respective cut-off points of all continuous variables were

systematically defined by applying an optimization algorithm as similarly described earlier (21).

Briefly, differences in OS were analyzed in the discovery cohort using an approach of maximally

selected p-values based on log rank tests at different cut-off points. A primary cut-off candidate

was that resulting in the lowest significant p-value comparing patients with values above vs




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below the respective cut-off point. A secondary cut-off candidate was that resulting in the lowest

significant p-value comparing patients within the groups defined by the primary cut-off

candidate. Cut-off points were only tested if the smaller group comprised at least 10% of all

patients of the discovery cohort with available data. More details about the cut-off point selection

algorithm are presented in Supplementary Methods.

The analysis of the confirmation cohort (recruitment until December 24th 2014) aimed to confirm

the previously defined candidates. Next, a combination model based on confirmed candidates

was defined by analysis of all patients of the combined discovery and confirmation cohort. The

analysis of an additional validation cohort (recruitment until April 30th 2015) finally aimed to

validate the combination model.

Finally, clinical responses were collected as assessed by the investigators of the respective

clinical site and categorized as either complete response (CR), partial response (PR), stable

disease (SD) or progressive disease (PD) according to RECIST V1.1 criteria (22). The best

overall response (bOR) was defined as the best achieved response from the start of

pembrolizumab and to the date of progressive disease or start of a new systemic treatment. All

tumor assessments within this time period were considered. Based on the bOR we analyzed the

proportion of patients who had an objective response (complete or partial response, patients

without a disease assessment following initiation of pembrolizumab were considered to be non-

responders).

Statistics

Follow-up time was defined from the date of the first dose to the date of last known contact or

death. Survival probabilities and median survival with 95% confidence intervals (CI) were




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estimated according to the Kaplan-Meier method, and compared using log rank tests. Only deaths

due to melanoma were considered and other causes of death were regarded as censored events,

except for the analysis presented in Supplementary Figure 2. Here, death from any reason was

considered as “event”. Cox proportional hazard analyses using stepwise procedures were used to

determine the relative impact of confirmed factors. Patients with missing data in variables

analyzed in the given Cox regression model were excluded. Results are described by means of

hazard ratios (HR) with 95% CIs, and P-values are based on the Wald test. The concordance

index (c-index) was calculated for different models as a measure of the discriminatory ability that

allows comparison of models. A model with a c-index=0.5 has no predictive value, a model with

a c-index =1 would allow a perfect prediction of patient outcome (23). The concordance index

was analyzed using the survConcordance function in the survival package for R.

Associations between best overall response and biomarker categories were analyzed by Chi

square and Fisher´s exact tests. Throughout the analysis, P-values <0.05 were considered

statistically significant. All statistical tests were two-sided. Analyses were carried out using SPSS

Version 22 (IBM, USA) and R 3.2.1 (R Foundation for Statistical Computing, Vienna Austria).




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Results

Patients and treatments

A total of 616 patients treated with pembrolizumab were included (Table 1). The median age was

60 years, and 40.6% were male. Of 578 patients with sufficient data for classification according

to AJCC 2009 (24), 483 were assigned to the M-category M1c (83.6%), 59 to M1b (10.2%), 25

to M1a (4.3%), and eleven patients had unresectable stage III disease (1.9%). Treatment was

mainly administered in the early access program (65.3%; NCT02083484) or in the Keynote-002

study (17.7%; NCT01704287) (5). Fifty-four patients were treated with commercial

pembrolizumab after FDA marketing approval (8.8%), 42 patients were treated in the Keynote-

001 study (6.8%; NCT01295827) (1, 4), and nine were treated in the Keynote-006 study (1.5%;

NCT01515189). 97.9% had received at least one prior systemic treatment including ipilimumab,

while 2.1% of patients received pembrolizumab as first-line therapy. Of 389 patients with

available data on the bOR, 28.3% experienced a clinical response (5.4% CR and 22.9% PR).

19.5% of patients had a bOR of SD, and 36.2% had PD. 15.9% of patients had no radiologic

tumor assessment before death due to melanoma progression. Among the whole population, 224

(36.4%) of patients died during follow up. 219 died from melanoma progression, two patients

died from voluntary physician-assisted ending of life and another three from cerebral strokes.

Median follow-up was 5.5 months for patients who were alive at the last follow-up, and 2.9

months for those who died.

Identification and confirmation of baseline factors associated with OS

The pattern of visceral metastasis, gender and 13 continuous variables (such as age or blood

counts) were initially investigated in the discovery cohort (n=177) using a systematic approach of




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cut-off optimization for continuous factors (Supplementary Table 2). No correlation with OS was

observed in the discovery cohort for gender, age, absolute and relative monocyte counts, and

absolute basophil count (all P≥0.05). These factors were no longer considered in subsequent

analyses. Significant negative correlations with OS were observed for a high LDH-ratio

(P<0.001), high leucocyte count (P=0.02) and high absolute and relative neutrophil counts (both

P<0.001). Significant positive correlations with OS were observed for high absolute (P=0.02) and

relative (P<0.001) eosinophil counts, high absolute (P=0.002) and relative (P<0.001) lymphocyte

counts, and high relative basophil counts (P=0.001).

In the confirmation cohort (n=182), nine factors were again significantly associated with

prognosis using the following previously defined cut-off points for categorization: LDH-ratio

≤1.7 vs >1.7 and ≤2.5 vs >2.5, absolute leucocyte count <8750 vs ≥8750, absolute neutrophil

count <6050 vs ≥6050, relative neutrophils count <61.5% vs ≥61.5% and <77.5% vs ≥77.5%,

absolute eosinophil count <150 vs ≥150, relative eosinophil count <1.5% vs ≥1.5%, relative

lymphocyte <17.5% vs ≥17.5% and <26.5% vs ≥26.5%, and relative basophil counts <0.5% vs

≥0.5% (all P<0.05). Patients with unresectable stage III disease or metastases located to distant

lymph nodes/soft tissue or lung had a significant longer OS as compared to patients with other

visceral metastases in the discovery and confirmation cohorts (both P<0.001).

Definition of a combination model for prognosis after pembrolizumab

Cox regression analysis was performed including all patients of the combined discovery and the

confirmation cohort with complete data (n=346) to determine the relative impact of confirmed

factors. Complete data were available for 170 out of 177 patients from the discovery cohort and

for 176 out of 182 patients from the confirmation cohort. As two cut-off points were confirmed

for the LDH-ratio, relative lymphocyte count (RLC) and relative neutrophil count, altogether




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twelve variable/cut-off combinations were considered in the multivariate model. A LDH-ratio

≤2.5 (HR 2.5; P<0.001), RLC ≥17.5% (HR 1.9; P<0.001), relative eosinophil count (REC) ≥1.5%

(HR 2.2; P<0.001), and no involvement of visceral metastasis other than lung (HR 2.5; P<0.001)

were factors independently associated with longer OS (Table 2 and Supplementary Table 2).

None of the 8 remaining variable/cut-off combinations added further significant independent

prognostic information (all P≥0.05).

Next, we analyzed prognosis according to the number of favorable baseline factors present

considering the four independent factors as a combination model. 46 patients (13.3%) had

favorable results in all 4 baseline factors and a 1-year survival probability of 87.4%. Patients

who had three (n=103; 29.8%) or two (n=99; 28.6%) factors present had a 1-year OS of 68.5%

and 46.4%, respectively. In contrast, 1-year OS probability was 12.7% or 15.7% for patients with

1 (n=68; 19.7%) or 0 (n=30; 8.7%) favorable factors, respectively (Supplementary Figure 1A).

Independent validation of the combination model

In the validation cohort (n=257) all four variables were again associated with OS (all P<0.001;

Supplementary Table 2) and had again independent impact on prognosis in Cox regression

analysis (Table 2) considering 166 of 257 patients with available data for all 4 factors. The risk of

death was 2.4-fold (P=0.003) and 2.2-fold (P<0.001) higher for patients in the validation cohort

with RLC <17.5% and REC <1.5%, respectively. The presence of visceral metastases other than

lung was also associated with OS (HR 3.5; P=0.008). The LDH-ratio >2.5 was the strongest

independent factor with a 6.1 times increased risk of death (P<0.001). Overall survival according

to the number of favorable baseline factors in the validation cohort is presented in Supplementary

Figure 1B.




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Confirmed associations with overall survival in all patients treated with pembrolizumab

In addition to LDH, REC, RLC and the pattern of visceral metastasis as considered in the

combination model, we analyzed the other 8 confirmed categorizations in the validation cohort as

well. All but AEC (P=0.0763) were again significantly associated with OS (all P<0.05).

Univariate analysis considering all 616 patients treated with pembrolizumab is presented in Table

3. The 1-year OS was highest with 81.9% for patients with unresectable stage III melanoma or if

distant metastases were limited to soft-tissue and/or lungs. LDH was a strong factor for

stratifying patients according to prognosis into three distinct groups. The median survival was

16.1 months for patients with baseline LDH within normal limits or up to 1.7-fold elevation. In

contrast, it decreased to 8.7 and 2.3 months for those with >1.7-fold or >2.5-fold elevation above

the upper limit of normal, respectively. RLC ≥26.5% identified patients with a survival

probability of 77.4% at one year. High absolute or relative eosinophil counts and high relative

basophil count were also strongly associated with favorable outcome. High absolute or relative

neutrophil counts and high absolute leucocyte count were negatively correlated with OS (all

P<0.001).

Overall survival and proportion of patients with objective response according to LDH,

pattern of distant metastasis, RLC and REC as considered in the combination model

Considering all 512 patients with complete data (Table 2), those with LDH-ratio >2.5 had a HR

of 2.8 after initiation of pembrolizumab compared to patients with LDH-ratio ≤2.5 (P<0.001). HR

of patients with distant metastasis in visceral organs other than lung/soft-tissue was 2.7

(P<0.001). Moreover, RLC <17.5% was independently associated with poor OS compared to a

higher baseline RLC (HR 2.0; P<0.001). Finally, patients presenting REC <1.5% had an

increased risk of death (HR 2.0; P<0.001). OS according to the categories of the four single




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factors is presented in Figure 1. The relative proportion of all possible combination groups and

the respective survival analyses accounting for LDH, pattern of visceral metastasis, RLC and

REC are presented in Supplementary Table 3.

The count of favorable pre-treatment values (LDH-ratio ≤2.5, REC ≥1.5%, RLC ≥17.5% and

absence of visceral metastasis other than lung) was strongly associated with long OS after start of

pembrolizumab (Figure 2). Four or three favorable baseline values were observed in 70 (13.7%)

and 160 (31.3%) patients, respectively. These patients had an very favorable outcome with a 1-

year OS probability of 83.9% [70.3%;97.5%] and 68.1% [55.1%;81.1%]. 141 patients (27.5%)

had 2 favorable baseline values and a 1-year survival probability of 47.9% [34.8%;61.0%]. In

contrast, 1 or no favorable baseline values were present in 109 and 32 patients (21.3%, 6.3%).

These patients had a poor prognosis with a 14.3% [3.5%;25.1%] and 14.7% [1.3%;28.2%]

probability to survive 1-year, respectively (Figure 2A). The discriminatory ability (c-index) of

this model was 0.782.

As five patients died from reasons other than progressive melanoma, we additionally analyzed

OS according to the combination model considering death from any reason as “event”. Using this

approach, the discriminatory ability was marginally higher (c-index=0.783), and differences in

OS remained significant in all pairwise comparisons (all p<0.05; Supplementary Figure 2).

The proportion of patients experiencing an objective response (patients with PR or CR as best

overall response according to RECIST V1.1) strongly correlated with the number of favorable

baseline values in 389 of 512 patients with available data. An objective response was observed in

58.3%, 38.4%, or 24.3% of patients with 4, 3, or 2 favorable baseline values. In contrast, an

objective response was only seen in 7.7% or 3.7% of patients with only 1 or 0 favorable baseline

values, respectively (Figure 2B).




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The association of the number of favorable baseline values with OS and bOR was also strong if

the well-established prognostic markers LDH and the pattern of visceral metastasis were not

considered in the combination model: patients with REC ≥1.5% and RLC ≥17.5% had a 1-year

survival probability of 70% compared to 49% for patients with either REC ≥1.5% or RLC

≥17.5% and to 22% for those with REC <1.5% and RLC <17.5% (p<0.001 for all pairwise

comparisons; Figure 3A). Moreover, significant differences in the proportion of patients with

objective responses were observed between the respective groups (42.5%, 23.6%, and 12.5%;

42.5% vs 23.6%: p<0.001; 23.6% vs 12.5%: p=0.042; Figure 3B).

The independent prognostic impact of RLC and REC in addition to LDH and the pattern of

visceral metastasis was analyzed in a separate Kaplan-Meier analysis after stratification of

patients according to the latter two prognostic factors. An independent additional impact was

observed in patients with LDH ratio ≤2.5 and no involvement of visceral metastasis other than

lung (Supplementary Figure 4A) or if either one condition was true (Supplementary Figure 4B).

The additional impact of REC and RLC was not statistically significant for patients with LDH-

ratio >2.5 and visceral metastases not limited to the lungs (Supplementary Figure 4C).

This combination model was based on the number of favorable baseline factors and thus did not

account for the differences in the relative impact among the four considered factors. Due to the

observed differences in HRs ranging from 2.8 (LDH-ratio >2.5) to 2.0 for REC<17.5% and

RLC<1.5, other combination models accounting for the relative impact were evaluated as well

(Supplementary Figure 4). The discriminatory abilities (c-indices) of the 5 evaluated alternative

models ranged between 0.779 and 0.785, and therefore compared similarly to the initial

combination model based on the number of favorable baseline factors (c-index=0.782; 95%

confidence interval 0.738 - 0.825).




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Discussion

In the current study, the LDH-ratio, the pattern of visceral involvement, RLC and REC, represent

four baseline factors independently associated with OS of melanoma patients treated with

pembrolizumab. Both LDH and pattern of visceral metastases are well established prognostic

factors of advanced melanoma patients and are incorporated into the AJCC staging

classification.(24) An elevated pretreatment LDH has also been previously described to correlate

negatively with OS in patients treated with ipilimumab (15, 16, 25) and pembrolizumab (26).

Thus, LDH’s negative association with outcomes following pembrolizumab, as observed here,

was not surprising. However, high pre-treatment RLC and REC were also independently

associated with improved OS. Differential blood counts have not been investigated as biomarkers

for pembrolizumab thus far. For ipilimumab, high ALC (15), low absolute monocyte count, and

high RLC (25) at baseline, and increasing ALC during treatment (16), indicate favorable

prognosis. No change of ALC or association with responses was reported for patients treated with

nivolumab and ipilimumab in combination (17).

Different subsets of T cells targeting tumor-associated- or neo-antigens are involved in

immunological melanoma rejection (27, 28). PD-1 is expressed on T cells after initial antigen

stimulation (29). After persistent antigen stimulation, PD-1 signaling results in secondary

suppression of T cells to prevent sustained proliferation and activation. Engagement occurs after

binding its ligands PD-L1 or PD-L2 mainly expressed on stromal cells or antigen presenting cells

in the periphery (30, 31). This physiological process can be utilized by cancer cells to escape

from T cell rejection by expression of PD-L1 (32, 33). Blocking antibodies targeting PD-1 or its

counterpart PD-L1 can prevent this form of T cell inactivation (1, 10, 18, 34) ensuring sustained

anti-tumor activity. Of note, the number of tumor mutations and clinical outcome following

pembrolizumab were recently reported to correlate in patients with non-small cell lung cancer




21

(NSCLC) suggesting a prominent role of T cells targeting neo-epitopes (35). Thus, T cells

represent the main cellular target of pembrolizumab and are required for effective

pembrolizumab-induced tumor rejection (18). As T cells represent the majority of lymphocytes

this is in agreement with the observed positive correlation of high lymphocyte counts throughout

our analysis with OS following pembrolizumab.

Eosinophils have important functions for tumor surveillance and were described as effectors for

tumor rejection in animal models (36-38). For ipilimumab, high REC at baseline was correlated

with favorable OS (25, 39) and early increases during treatment was associated with improved

clinical responses (40). Nevertheless, a potential role for a beneficial mode of action of

pembrolizumab, which may be hypothesized based on the prognostic impact of REC in our study,

needs to be investigated in greater detail.

Based on the independent prognostic impact, we analyzed OS stratified by the number of

favorable results considering LDH-ratio, pattern of visceral involvement, RLC and REC. The

proposed combination model allowed the identification of patient subgroups with extremely

different prognoses following pembrolizumab. 13.7% of patients had favorable values in all 4

factors and an 84.2% 1-year OS probability. In strong contrast, prognosis was poor for

individuals with 0-1 favorable factors, who had a chance of only 14.8% to survive the first year

after start of pembrolizumab and a median survival of 2.6 months.

Our study does not address the key question of whether the number of unfavorable factors

according to the proposed combination model is generally prognostic for advanced melanoma

patients or specifically predictive for outcome after pembrolizumab. Based on the consideration

of LDH and the pattern of visceral metastasis in the combination model, an association with

prognosis can be assumed also in other clinical situations. However, the assumed prognostic

association of these factors does not exclude the possibility of additional predictive impact for




22

outcome after pembrolizumab. First, after omitting the well-established prognostic markers LDH

and the pattern of visceral metastasis, a reduced model limited to RLC and REC was still strongly

correlated with prognosis (Figure 3). Second, since lymphocytes are centrally embedded in the

mode of action of pembrolizumab, the correlation of high RLC and favorable OS, especially for

patients with low visceral tumor burden and low LDH (Supplementary Figure 3A), may

ultimately be expected to be related to predictive effects after pembrolizumab. No prognostic role

has yet been established for RLC in advanced melanoma patients. Third, a strong correlation of

the number of favorable baseline factors with bOR was observed here, in addition to the

association with OS. Only patients with partial or complete response were considered as

objective responders in the current study. In contrast to the observation of improved OS, which is

susceptible for confounding by subsequent treatments, and may be only prognostic, the

observation of an objective response is more likely causally linked to the beneficial mode of

action of the applied treatment. Thus the correlation of the count of favorable baseline biomarkers

with clinical responses and OS, as observed here, underlines a potential predictive role of the

biomarker signature in addition to its described prognostic role.

In total, this study included 616 patients from three independent cohorts from 30 sites and six

different countries, minimizing the risk that results are confounded by patient selection, regional-

or site-specific influences. Nevertheless, such a bias based on the retrospective design cannot be

excluded. Potential confounding effects of the treatment line or ipilimumab pretreatment could

not be analyzed due to the low number of ipilimumab-naïve patients who received

pembrolizumab as first treatment for unresectable disease (n=13). Other factors which might also

impact outcome (e.g. the ECOG performance status) were not analyzed here. Finally, a bias

induced by patient selection procedures for treatment with pembrolizumab or for provision of

data for this study cannot be excluded. Further prospective validation accounting for these




23

limitations of our study is warranted. In strong contrast to the analysis of PD-1/PD-L1 expression

in tumor tissue, the assessment of the count of favorable baseline peripheral blood routine factors

is feasible and if ultimately shown to be predictive of outcome, could be easily integrated into

daily clinical practice.

Nevertheless, based upon these data alone, the proposed combination model should not be used

with the purpose to guide treatment decisions at this time. In addition to the inherent study

limitations based on the retrospective design, it remains to be clarified as to whether patients with

zero or one favorable factor (poor response rate and OS group) still derived some treatment

benefit from pembrolizumab that they may not have had with alternative treatment. This question

will only be able to be addressed in randomized controlled trials involving pembrolizumab and a

comparator (e.g. BRAF/MEK inhibitors, chemotherapy, nivolumab, ipilimumab). In spite of the

broad availability of the considered routine factors and the principle possibility to perform such

studies in a retrospective setting, the value of analysis of historic cohorts is questionable due to

inevitable imbalances between the cohorts and a high risk of bias. In contrast, the analysis of

outcome according to baseline biomarkers combinations of patients treated with pembrolizumab

compared to a control group in a randomized controlled trial setting may more conclusively

clarify if there is predictive impact or not. This is in principle possible in the Keynote-002 trial or

Keynote-006 trials. In the Keynote-002 trial, ipilimumab-refractory melanoma patients were

randomized to receive two different doses of pembrolizumab or investigator-choice

chemotherapy (5). In the Keynote-006 trial, ipilimumab-naive patients were randomized to

receive pembrolizumab using two different dosing schedules or ipilimumab (3). Both trials

showed a superiority regarding progression free survival (both trials) or OS (Keynote-006) of

patients treated with pembrolizumab compared to alternative treatments. As all baseline data

required for the proposed combination model including RLC and REC have been collected in the




24

respective trial databases, a retrospective analysis of these data would be possible in principle.

We are currently planning to perform such studies in collaboration with the sponsoring company.

Ultimately, prospective testing in a randomized controlled trial is necessary to prove or exclude a

definite predictive impact.

In conclusion, low pre-treatment values of LDH, limited visceral tumor burden, high REC and

high RLC are independently associated with favorable OS of melanoma patients treated with

pembrolizumab. Patients with favorable results in all markers have an excellent prognosis, while

significant treatment advances are still needed for patients with 3-4 unfavorable baseline values,

whose outcome remains poor even in the era of new immunotherapy strategies such as

pembrolizumab. Whether the proposed baseline biomarker signature has specific predictive

impact for outcome following pembrolizumab or rather represents a prognostic marker

combination for advanced melanoma patients is unclear but provides a foundation for

investigation in randomized controlled trials.




25

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30

Table Legends:

Table 1: Patient and treatment characteristics. AJCC: American Joint Committee on Cancer;

LDH: lactate dehydrogenase; IQR: interquartile range

Table 2: Cox regression analysis. LDH: lactate dehydrogenase; CI: confidence interval

Table 3: Overall survival of patients treated with pembrolizumab (cohorts combined)

according to confirmed prognostic factors. LDH: lactate dehydrogenase; CI: confidence

interval; n.r.: not reached




31

Figure Legends:

Figure 1: Overall survival according to confirmed baseline factors independently associated

with outcome following pembrolizumab. Kaplan-Meier curves of overall survival considering

patients of all three cohorts of patients treated with pembrolizumab (n=616) according to the

pattern of distant metastasis (A), LDH-ratio (the measured LDH serum concentration divided by

the upper limit of normal) (B), relative lymphocyte count (C) and relative eosinophil count (D).

Censoring is indicated by vertical lines; P-values were calculated by log rank statistics.

Figure 2: Overall survival and proportion of patients with objective response following

pembrolizumab according to the count of favorable baseline factors. LDH, pattern of distant

metastases, relative lymphocyte count and relative eosinophil count) had independent prognostic

impact in the combined discovery and confirmation cohort, as well as in the validation cohort.

Kaplan Meier curves for OS are presented for all patients according the count of favorable pre-

treatment values considering those four factors. Among 512 patients with complete data, the

median survival was 16.9, 10.8, 4.2, 1.4 months for patients with 0, 1, 2, or 3 favorable factor(s),

respectively. Median survival was not reached for 13.7% of patients who had favorable values in

all four factors. Censoring is indicated by vertical lines (A). The proportion of patients with

objective response (PR or CR as best overall response according to RECIST V1.1) in 389 of 512

patients with available data is presented according to the number of favorable baseline factors.

The differences between the categories were all statistically significant (p<0.05) in pairwise

comparisons using Chi-Square/Fisher´s exact tests except the difference between patients with 0

and 1 favorable factor (p=0.674; B).




32

Figure 3: Overall survival and proportion of patients with objective response following

pembrolizumab according to the relative lymphocyte and eosinophil counts. LDH, pattern of

distant metastases, relative lymphocyte count and relative eosinophil count had independent

prognostic impact in 512 patients with available data in all four baseline factors. Here, the

analysis is limited to the impact of relative lymphocyte and eosinophil counts. The pattern of

visceral metastasis and LDH are not considered here, as these are well-established prognostic

markers. Kaplan Meier curves for OS are presented according the count of favorable pre-

treatment values. Censoring is indicated by vertical lines. The differences in OS between

categories were significant in all pairwise comparisons (all log rank p<0.001; A). The proportion

of patients with objective response (PR or CR as best overall response according to RECIST

V1.1) in 389 of 512 patients with available response data is presented according to the number of

favorable baseline factors. An objective response was observed in 42.5%, 23.6%, and 12.5% of

patients with 2, 1, or 0 favorable baseline values. The differences between the categories were all

statistically significant (p=0.042 for 0 vs.1 favorable factor; p<0.001 for 1 vs 2 favorable factors)

using Chi-Square/Fishers´s exact tests (B).




1

Table 1

Discovery Cohort 1 (n=177)

ConfirmationCohort 2 (n=182)

Validation Cohort 3 (n=257)

Total (n=616)

n (%) n (%) n (%) n (%)

Gender Male 71 40.1 78 42.9 101 39.3 250 40.6 Female 106 59.9 104 57.1 156 60.7 366 59.4

Age

≤ 50 years 42 23.7 54 29.7 55 21.4 151 24.5 51 - 60 years 45 25.4 50 27.5 63 24.5 158 25.6 61 - 70 years 44 24.9 49 26.9 77 30.0 170 27.6 ≥ 70 years 46 26.0 29 15.9 62 24.1 137 22.2 Median age, IQR(years) 61, 51-71 58, 48-67 62, 52-70 60, 51-69

M category (AJCC 2009)

Unresectable stage III 3 1.7 6 3.3 2 0.9 11 1.9 M1a 2 1.1 7 3.8 16 7.3 25 4.3 M1b 24 13.6 19 10.4 16 7.3 59 10.2 M1c 147 83.5 150 82.4 186 84.5 483 83.6 Unknown 1 37 38

LDH Normal 76 43.9 93 51.1 96 57.8 265 50.9 Elevated 97 56.1 89 48.9 70 42.2 256 49.1 Unknown 4 91 95

Visceral involvement

Distant lymph nodes/soft tissue 4 2.3 11 6.0 38 14.8 53 8.6

Lung 41 23.2 28 15.4 47 18.3 116 18.8 Other organs 129 72.9 137 75.3 170 66.1 436 70.8 None (unresectable stage III) 3 1.7 6 3.3 2 0.8 11 1.8

Prior ipilimumab

No 6 3.4 3 1.6 4 1.6 13 2.1 Yes 171 96.6 179 98.4 253 98.4 603 97.9

Line of treatment

First line 6 3.4 3 1.6 4 1.6 13 2.1 Second line or later 171 96.6 179 98.4 253 98.4 603 97.9

Treatment schedule of pembrolizumab

10 mg/kg - 2 weeks 2 1.1 1 0.5 7 2.7 10 1.6 10 mg/kg – 3 weeks 1 0.6 24 9.3 25 4.1 2 mg/kg – 3 weeks 117 66.1 134 73.6 216 84.0 467 75.8 2 or 10 mg/kg - 3 weeks 54 30.5 45 24.7 10 3.9 109 17.7 10 mg/kg - 2 or 3 weeks 3 1.7 2 1.1 5 0.8

Treatment background

Commercial 54 21.0 54 8.8 Early access program 117 66.1 134 73.6 151 58.8 402 65.3 Keynote-001 42 16.3 42 6.8 Keynote-002 54 30.5 45 24.7 10 3.9 109 17.7

Keynote-006 6 3.4 3 1.6 9 1.5 AJCC: American Joint Committee on Cancer; LDH: lactate dehydrogenase; IQR: interquartile range




Table 2

Variable Categories

Combined discovery&confirmation

cohorts (n=346)

Validation cohort (n=166) All patients (n=512)

Hazard ratio [95% CI]

Wald test P-value

Hazard ratio[95% CI]

Wald test P-value

Hazard ratio[95% CI]

Wald test P-value

Visceral involvement

Unresectable stage III, distant lymph

nodes/soft tissue, lung 1.0

<0.001 1.0

0.008 1.0

<0.001 Other visceral involvement 2.5[1.5;4.1] 3.5[1.4;8.9] 2.7[1.8;4.2]

LDH-ratio ≤2.5 1.0 <0.001 1.0 <0.001 1.0 <0.001 >2.5 2.5 [1.7;3.6] 6.1[3.1;12.3] 2.8 [2.0;3.9] Relative lymphocyte count

<17.5% 1.9 [1.3;2.8] <0.001 2.4 [1.4;4.4] 0.003 2.0 [1.5;2.8] <0.001 ≥17.5% 1.0 1.0 1.0 Relative eosinophil count

<1.5% 2.2 [1.5;3.1] <0.001 2.2 [1.2;3.9] 0.006 2.0 [1.5;2.8] <0.001 ≥1.5% 1.0 1.0 1.0 LDH: lactate dehydrogenase; CI: confidence interval.

Research.

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R-16-0127


Table 3

Variable Total Categories n % % dead

Univariate Kaplan Meier Estimates Median survival

time (months)

1-year survival rate [95% CI]

(%)

Log-rank

P-value

All patients 616 100 35.6 15.5 54.5 [48.8; 60.1]

Visceral involvement 616

Unresectable stage III, distant lymph

nodes/soft tissue, lung180 29.2 17.7 n.r.

81.9 [74.4; 89.5] <0.001 Other visceral involvement 436 70.8 42.9 8.8 40.7 [33.3; 48.2]

LDH-ratio 521 ≤1.7 402 77.2 28.1 16.1 61.0 [53.6; 68.4]

<0.001 ≤2.5 38 7.3 55.3 8.7 28.0 [8.9; 47.2]>2.5 81 15.5 75.3 2.3 16.2 [5.5; 26.9]

Absolute leucocyte count 614

<8750/µL 423 68.9 29.3 17.6 63.2 [56.8; 69.7]<0.001 ≥8750/µL 191 31.1 49.2 7.1 34.0 [23.4; 44.5]

Absolute neutrophil count 607 <6050/µL 407 67.1 27.5 17.6 63.9 [57.1; 70.7] <0.001

≥6050/µL 200 32.9 50.5 6.4 35.6 [25.5; 45.7]Absolute eosinophil count 607

<150/µL 314 51.7 42.4 9.7 48.3 [40.8; 55.9]<0.001 ≥150/µL 293 48.3 27.3 17.6 61.8 [53.1; 70.4]

Relative lymphocyte count 607

<17.5% 287 47.3 48.4 7.0 38.9 [30.6; 47.2]<0.001 ≥17.5% 202 33.3 27.2 19.0 63.4 [53.9; 73.0]

≥26.5% 118 19.4 16.1 n.r. 77.4 [65.2; 89.5]

Relative neutrophil count 607

<61.5% 144 23.7 16.0 n.r. 76.5 [65.1; 87.9]<0.001 ≥61.5% 299 49.3 32.8 16.0 56.9 [48.8; 65.0]

≥77.5% 164 27.0 56.1 4.9 32.2 [22.2; 42.1]Relative eosinophil count 607 <1.5% 225 37.1 55.1 5.8 35.3 [26.8; 43.8] <0.001 ≥1.5% 382 62.9 23.3 19.6 66.7 [59.4; 74.0]Relative basophil count 607 <0.5% 342 56.3 45.0 8.8 45.4 [38.2; 52.7] <0.001 ≥0.5% 265 43.7 22.3 n.r. 67.6 [58.6; 76.5]

LDH: lactate dehydrogenase; CI: confidence interval; n.r.: not reached




Figure 1




Figure 2




Figure 3




Published OnlineFirst May 16, 2016.Clin Cancer Res Benjamin Weide, Alexander Martens, Jessica C Hassel, et al. with pembrolizumabBaseline biomarkers for outcome of melanoma patients treated

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